Our paper investigates the temporal upsampling problem. While previous approaches investigate in the framework of compressive sensing [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, we formulate our work as fusing event streams with intensity images to obtain a temporally dense video. Compared to existing literature [8]} which integrates event counts per pixel across time, our differentiable model utilizes “tanh” functions as event activation units and imposes sparsity constraints on both spatial and temporal domain.
